Detection of three-dimensional (3D) changes using a plurality of images is a very difficult problem. The main reason is that two-dimensional (2D) data must be used to assess the 3D changes. 3D change detection algorithms fall into three different classes:                1) those based on comparing images with each other;        2) those based on recovering 3D structures from the 2D images and comparing the reconstructions with each other; and        3) those that attempts to directly compare images to a 3D reference model of the scene.        
Algorithms of the first class, based on image comparison generally use one image as a reference while another is used to determine if changes have occurred. Unfortunately, intensity changes do not necessarily imply changes in the geometry of the scene: intensity variations might actually be caused by variations in the viewing/illumination conditions or in the reflectance properties of the imaged surfaces. Such algorithms are therefore not robust in general. In addition, they do not permit the evaluation of the importance of 3D changes
Algorithms of the second class, based on reconstruction use imaging data to infer the geometry of the scene or, in other words, to construct a 3D model. A comparison is then performed with a 3D model that serves as a reference. Significant differences between the reference model and the 3D reconstruction are considered as changes. Unfortunately, the reconstruction operation amounts to solving the stereo vision problem, a significantly difficult challenge.
Finally, algorithms of the third class directly compare images to a 3D model. The scene integrity is verified by matching image features to model features. The use of features allows the simplification of the comparison between the two different scene representations. Unfortunately, such algorithms suffer from the limitation of only processing very restricted regions of the scene, i.e. those that present the selected features. Therefore, changes that lie outside of these regions cannot be detected.